A framework to proactively consider road safety within the road planning process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
All too often, engineering strategies aimed at improving road safety are reactions to existing problems that occur after a road has been designed and built. Targeting problem locations and developing plans to reduce collisions are vital and have proven to be very successful. Transportation professionals, however, should also take a proactive approach to address road safety before problems emerge. This paper describes an evolving need of how to deal with road safety in a proactive manner. Although a proactive approach should improve the overall safety performance, there is currently a poor understanding of how to proactively plan for road safety. Several logistical and technical obstacles hinder the effective planning for road safety. Each of these obstacles is presented in detail, followed by a description of the opportunity to overcome each obstacle. The paper also includes the results of a case study used to demonstrate the proposed process. A proactive approach to road safety complements traditional, reactive methods currently in use. Significant progress will be realized once safety professionals shift their focus from fixing existing problems to helping plan roads that attempt to be problem free. The net result should be a safer road system.Key words: proactive road safety, safety audits, safety planning, safety evaluation, safety improvements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it